A stabilizing iteration scheme for model predictive control based on relaxed barrier functions

We propose and analyze a stabilizing iteration scheme for the algorithmic implementation of model predictive control for linear discrete-time systems subject to polytopic input and state constraints. The required on-line optimization makes use of a relaxed barrier function based problem formulation and performs only a limited, possibly small, number of optimization algorithm iterations between two consecutive sampling instants. The optimization algorithm dynamics as well as the resulting suboptimality of the applied control input are taken into account explicitly in the stability analysis, and the origin of the resulting overall closed-loop system, consisting of state and optimization algorithm dynamics, is proven to be asymptotically stable. The corresponding constraint satisfaction properties are also analyzed. Both the theoretical results and a presented numerical example illustrate the fact that asymptotic stability as well as a satisfactory closed-loop performance may be achieved independently of the number of optimization algorithm iterations, thus leading to a novel class of stabilizing MPC algorithms.